CPF: Conditional Particle Filter

In jeremyhengjm/UnbiasedScore: Unbiased estimators for partially observed diffusions

Conditional Particle Filter

Description

Runs a conditional particle filter.

Usage

CPF(
  model,
  theta,
  discretization,
  observations,
  nparticles,
  resampling_threshold = 1,
  ref_trajectory = NULL,
  treestorage = FALSE
)

Arguments

model	a list representing a hidden Markov model, e.g. hmm_ornstein_uhlenbeck
theta	a vector of parameters as input to model functions
discretization	list containing stepsize, nsteps, statelength and obstimes
observations	a matrix of observations, of size nobservations x ydimension
nparticles	number of particles
resampling_threshold	ESS proportion below which resampling is triggered (always resample at observation times by default)
ref_trajectory	a matrix of reference trajectory, of size xdimension x statelength; if missing, this function runs a standard particle filter
treestorage	logical specifying tree storage of Jacob, Murray and Rubenthaler (2013); if missing, this function store all states and ancestors

Value

a matrix containing a new trajectory of size xdimension x statelength.

jeremyhengjm/UnbiasedScore documentation built on Nov. 17, 2023, 1:48 a.m.